Izamarlina Asshaari

Platinum-Incorporating Graphene Counter Electrode for In2O3-Based DSSC with Various Annealing Temperatures

Development of platinum incorporating graphene (pt/graphene) thin films was prepared by sol-gel method via chemical bath deposition (CBD). Indium oxide (In2O3) as photoanode and pt/graphene as counter electrode is used to analyse the characteristics and performance of dye-sensitized solar cell (DSSC). Different annealing temperatures of 200 oC, 250 oC and 300 oC were proposed for the counter electrode in this study. The changes in the structural properties were analyzed by means of X-ray Diffraction (XRD) and atomic force microscopy (AFM) analysis. AFM results indicated very rough surface area of graphene sheet where roughness values decreased linearly from 0.65 μm to 0.18 μm by an increment in annealing temperature. The In2O3-based DSSC exhibited good photovoltaic performance with power conversion efficiency (η), photocurrent density (Jsc), open circuit voltage (Voc) and fill factor (FF) of 0.47 %, 5.46 mA cm-2, 0.54 V and 0.36 respectively. The obtained structural and photovoltaic performance analysis was proposed as a suitable benchmark for pt/graphene counter electrode with In2O3-based DSSC.

Genetic Algorithm-Based Fatigue Data Editing Technique

Durability testing is an essential process node in automotive component design analysis. The test associated with loading history can be accelerated if the fatigue data editing approach is considered for simplifying the given history. Even though they have been proven, unfortunately, the existing editing techniques involve complex mechanisms (e.g. abrupt detection, Fourier transformation and wavelet analysis), which are complicated in nature and which demand high computational costs. Therefore, this paper is a proposal of a simple technique that makes use of the rule-based fatigue segment classifier when deciding which parts of the history need to be removed. Rules representing the new labelling practice have been generated based on the classification data mining framework. In the context of this study, a rule set represents a group of undiscovered relationship between time domain statistical parameters and damage level. A dataset consisting of an equal length of fatigue segments trained using a multi-objective approach called the Elitist Non-dominated Sorting in Genetic Algorithm (NSGA-II) for seeking several optimal sets of rules (i.e., classifiers) by maximizing predictive accuracy and comprehensibility. The number of attributes underlying the rule set is referred to for final classifier selection where the fitter solution serves as the proposed editing technique. Comparison results on strain-stress cycle properties for the edited history and the full-length version shows that the proposed technique is suitable for fatigue data editing. Moreover, it has an additional benefit that no prior requirement on the frequency or time-frequency analysis is needed, providing the damage level of fatigue segments rapidly and the discovering of linguistic knowledge as a novelty.